Carin, Lawrence and Baraniuk, Richard G. and Cevher, Volkan and Dunson, David and Jordan, Michael I. and Sapiro, Guillermo and Wakin, Michael B. (2011) Learning Low-Dimensional Signal Models. IEEE Signal Processing Magazine, 28 (2). pp. 39-51. ISSN 1053-5888 http://resolver.caltech.edu/CaltechAUTHORS:20110314-091724983
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Sampling, coding, and streaming even the most essential data, e.g., in medical imaging and weather-monitoring applications, produce a data deluge that severely stresses the available analog-to-digital converter, communication bandwidth, and digital-storage resources. Surprisingly, while the ambient data dimension is large in many problems, the relevant information in the data can reside in a much lower dimensional space.
|Additional Information:||© 2011 IEEE. Date of publication: 17 February 2011. Many graduate students contributed to the ideas and results reviewed in this article. The authors particularly acknowledge the contributions of Minhua Chen, Armin Eftekhari, John Paisley, and Mingyuan Zhou. The authors also thank the reviewers for a careful reading of the original version of this article and suggestions that led to a significantly improved final article.|
|Official Citation:||Carin, L.; Baraniuk, R.G.; Cevher, V.; Dunson, D.; Jordan, M.I.; Sapiro, G.; Wakin, M.B.; , "Learning Low-Dimensional Signal Models," Signal Processing Magazine, IEEE , vol.28, no.2, pp.39-51, March 2011 doi: 10.1109/MSP.2010.939733 URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5714381&isnumber=5714377|
|Usage Policy:||No commercial reproduction, distribution, display or performance rights in this work are provided.|
|Deposited By:||Tony Diaz|
|Deposited On:||16 Mar 2011 18:32|
|Last Modified:||16 Mar 2011 18:32|
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